NDLI: Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Content Provider	IEEE Xplore Digital Library
Author	Agudo-Peregrina, A.F. Hernandez-Garcia, A. Iglesias-Pradas, S.
Copyright Year	2012
Description	Author affiliation: Departamento de Ingeniería de Organización, Administración de Empresas y Estadística, Universidad Politécnica de Madrid, Spain (Agudo-Peregrina, A.F.; Hernandez-Garcia, A.; Iglesias-Pradas, S.)
Abstract	Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students' academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.
Starting Page	1
Ending Page	6
File Size	665508
Page Count	6
File Format	PDF
ISBN	9781467347433
e-ISBN	9788493981471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-29
Publisher Place	Andorra
Access Restriction	Subscribed
Rights Holder	UOLS
Subject Keyword	Context Electronic learning Data analysis Learning analytics Databases Humans Typologies Academic performance Real-time systems Interactions Data mining
Content Type	Text
Resource Type	Article

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